[go: up one dir, main page]

CN113866493A - Method for measuring voltage fluctuation and flicker caused by wind power - Google Patents

Method for measuring voltage fluctuation and flicker caused by wind power Download PDF

Info

Publication number
CN113866493A
CN113866493A CN202111238621.XA CN202111238621A CN113866493A CN 113866493 A CN113866493 A CN 113866493A CN 202111238621 A CN202111238621 A CN 202111238621A CN 113866493 A CN113866493 A CN 113866493A
Authority
CN
China
Prior art keywords
wind power
power
voltage fluctuation
frequency
interharmonic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111238621.XA
Other languages
Chinese (zh)
Inventor
刘云
吴彬
李振斌
霍现旭
马世乾
王峥
李树鹏
刘亚丽
崇志强
吴磊
于光耀
王天昊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Tianjin Electric Power Co Ltd, Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202111238621.XA priority Critical patent/CN113866493A/en
Publication of CN113866493A publication Critical patent/CN113866493A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
    • G01R19/2506—Arrangements for conditioning or analysing measured signals, e.g. for indicating peak values ; Details concerning sampling, digitizing or waveform capturing
    • G01R19/2509—Details concerning sampling, digitizing or waveform capturing

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Current Or Voltage (AREA)

Abstract

本发明涉及一种风电引起电压波动与闪变的测量方法，包括以下步骤：步骤1、利用基于最优窗加权修正的Burg算法对，对同步采样后的风电输出电压信号序列进行功率谱估计，并通过功率谱的分析可精确得到各间谐波分量的频率；步骤2、根据步骤1所得出的各间谐波分量的频率，对电压采样信号序列进行FFT分析，获得真实间谐波的幅值与相位；步骤3、根据步骤2计算获得的获得真实间谐波的幅值与相位，将原始信号经过视感度加权滤波器模拟灯‑眼‑脑频率响应特性后，获得从而得到由风电引起电压波动与闪变的测量结果。本发明能够提高风电场引起的电压波动及闪变的检测精确性。

Figure 202111238621

The invention relates to a method for measuring voltage fluctuation and flicker caused by wind power, comprising the following steps: Step 1. Using a Burg algorithm pair based on optimal window weighting correction to estimate the power spectrum of the synchronously sampled wind power output voltage signal sequence, And through the analysis of the power spectrum, the frequency of each interharmonic component can be accurately obtained; step 2, according to the frequency of each interharmonic component obtained in step 1, perform FFT analysis on the voltage sampling signal sequence to obtain the amplitude of the real interharmonic. value and phase; step 3. According to the amplitude and phase of the real interharmonic obtained by calculation in step 2, the original signal is obtained after passing through the visual sensitivity weighting filter to simulate the frequency response characteristics of the lamp-eye-brain, so as to obtain the frequency response characteristics caused by wind power Measurement results of voltage fluctuation and flicker. The invention can improve the detection accuracy of the voltage fluctuation and flicker caused by the wind farm.

Figure 202111238621

Description

Method for measuring voltage fluctuation and flicker caused by wind power
Technical Field
The invention belongs to the technical field of power quality monitoring of power systems, and particularly relates to a method for measuring voltage fluctuation and flicker caused by wind power.
Background
The wind power generator set is influenced by factors such as wind shearing effect, tower shadow effect and the like, fluctuation of specific frequency occurs in output power, so that inter-harmonic waves of the specific frequency are generated, and the damage of voltage fluctuation and flicker caused by the inter-harmonic waves is also wide. Inter-harmonics are the root cause of voltage fluctuations and flicker in wind farms.
With the increasing scale of wind power installations, the influence of the wind power installations on the power quality of a power grid is more and more obvious, so that the requirements on inter-harmonic detection of a wind power plant and voltage fluctuation and flicker analysis caused by the inter-harmonic are higher and higher. The voltage flicker phenomenon caused by wind power is random and influenced in many aspects, and the voltage flicker calculation and evaluation research difficulty caused by the wind power is higher due to the fact that the frequency domain distribution universality and the amplitude of inter-harmonics are weak and the like.
Some contents related to inter-harmonic detection algorithm and flicker caused by inter-harmonics are mentioned in relevant national standards, but the method has limitations, resolution is not high, and detailed inter-harmonic parameters cannot be obtained.
Therefore, in view of the harmfulness of voltage fluctuation and flicker of the wind power plant and the lack of the current analysis means, research on calculation and evaluation of the voltage fluctuation and flicker of the wind power plant needs to be strengthened, and a method for measuring the voltage fluctuation and flicker caused by wind power is invented.
No prior art publications that are the same or similar to the present invention have been found by search.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for measuring voltage fluctuation and flicker caused by wind power, and can improve the detection accuracy of the voltage fluctuation and flicker caused by a wind power plant.
The invention solves the practical problem by adopting the following technical scheme:
a method for measuring voltage fluctuation and flicker caused by wind power comprises the following steps:
step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component;
step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic;
and 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2.
Further, the specific steps of step 1 include:
(1) for a discrete signal x (n), the representation as a p-th order AR model is:
Figure BDA0003318399390000021
in formula (1): η (n) is zero mean variance σ2White noise of (2). a isk(k 1.., p) is the coefficient of the AR model of order p.
(2) The AR power spectral density of signal x (n) is:
Figure BDA0003318399390000022
an AR model of order p is equivalent to a linear predictor of order p, the parameters of the AR model are the coefficients of the linear predictor, and the variance σ2Minimum prediction error power equal to order ppTherefore, the power spectrum formula is equivalent to:
Figure BDA0003318399390000031
therefore, only the minimum prediction error power ρ is obtainedpAnd model parameters akThe power spectrum of the signal can be obtained;
(3) AR model parameter solution
Figure BDA0003318399390000032
Figure BDA0003318399390000033
Figure BDA0003318399390000034
E in formulae (4), (5) and (6)m(n)、bm(n) are the forward and backward prediction errors, respectively, for an order of m;ρmthe predicted error power when the order is m; k is a radical ofmThe reflection coefficient is the order m.
Initial value: prediction error
Figure BDA0003318399390000035
Initial value e of front and back prediction error0(n)＝b0(n) ═ x (n). The filter coefficients are calculated according to the Burg algorithm:
Figure BDA0003318399390000036
calculating the prediction error power:
ρm＝(1-|km|2)ρm-1 (8)
computing output
Figure BDA0003318399390000037
Finally obtaining the minimum prediction error power rhopAnd model parameters ak。
The power spectral density is obtained by substituting formula (3), and the frequency f of each inter-harmonic component contained in the original data can be accurately obtained1,f2,f3,...,fn。
Further, the specific steps of step 2 include:
(1) let fa，fbIs f1、f2、f3、...、fnOf the 2 frequency components having the smallest difference;
determining the two closest frequency components in the signal, denoted faAnd fbThe difference f between the two frequenciesab＝|fa-fbThe frequency resolution Δ f of the spectral line should at least satisfy:
Figure BDA0003318399390000041
minimum number of data points N for Fourier analysis of a signalminComprises the following steps:
Figure BDA0003318399390000042
intercepting N data of an original signal, and meeting the following requirements: n is an integer multiple of 1024 and N>Nmin；
(2) Performing FFT analysis on the intercepted data x (n) to obtain the amplitude and the phase of each inter-harmonic;
Figure BDA0003318399390000043
because the maximum frequency range of human perception to flicker does not exceed 0.05-35 Hz, the frequency band of inter-harmonic waves concerned by the invention is 15-85 Hz; after filtering, only f with the inter-harmonic frequency band within the range of 15-85 Hz is reserved1、f2、f3、...、fnThe amplitude and phase of each inter-harmonic, i.e.:
ui(t)＝Ui sin(ωit+θi)
(3) the original signal can be expressed as:
Figure BDA0003318399390000044
in the formula, the first part is fundamental wave, and the second part is inter-harmonic wave.
Further, the specific steps of step 3 include:
(1) the original signal is passed through a perceptibility weighting filter to simulate a lamp-eye-brain frequency response characteristic as follows:
Figure BDA0003318399390000051
and obtaining the final measurement result values of voltage fluctuation and flicker caused by wind power:
Figure BDA0003318399390000052
in the formula: g is a constant of the gain, and G is a constant of the gain,
ω0i＝ω0-ωi，θ0i＝θ0-θi。
the invention has the advantages and beneficial effects that:
the invention combines the flicker problem with the inter-harmonic, starts with researching the frequency spectrum analysis algorithm of the inter-harmonic, improves the precision of inter-harmonic detection and measures the related parameters of the inter-harmonic, thereby improving the calculation precision and speed of the frequency domain algorithm for flicker calculation. The invention deeply researches the mechanism of the voltage flicker phenomenon caused by wind power, takes a flicker calculation method and a flicker evaluation standard as final targets, and systematically researches the voltage fluctuation and flicker problem caused by the wind power.
Drawings
FIG. 1 is a hardware configuration diagram of an inter-harmonic detection apparatus;
fig. 2 is a software configuration diagram of the inter-harmonic detection apparatus.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a method for measuring voltage fluctuation and flicker caused by wind power comprises the following steps:
step 1, performing power spectrum estimation on a wind power output voltage signal sequence after synchronous sampling by using a Burg algorithm pair based on optimal window weighting correction, and analyzing a power spectrum to accurately obtain the frequency of each inter-harmonic component;
the method is mainly applied to non-real-time analysis of wind field voltage fluctuation and flicker, the time window can be set to be longer, and the frequency of each inter-harmonic component can be accurately obtained through analysis of the power spectrum.
The specific steps of the step 1 are as follows:
(1) for a discrete signal x (n), the representation as a p-th order AR model is:
Figure BDA0003318399390000061
in formula (1): η (n) is zero mean variance σ2White noise of (2). a isk(k 1.., p) is the coefficient of the AR model of order p.
(2) The AR power spectral density of signal x (n) is:
Figure BDA0003318399390000062
an AR model of order p is equivalent to a linear predictor of order p. The parameters of the AR model are the coefficients of the linear predictor, the variance σ2Minimum prediction error power equal to order pp. So the power spectrum formula is equivalent to:
Figure BDA0003318399390000063
therefore, only the minimum prediction error power ρ is obtainedpAnd model parameters akThe power spectrum of the signal is obtained.
(4) AR model parameter solution
Figure BDA0003318399390000064
Figure BDA0003318399390000065
Figure BDA0003318399390000071
E in formulae (4), (5) and (6)m(n)、bm(n) are the forward and backward prediction errors, respectively, for an order of m; rhomThe predicted error power when the order is m; k is a radical ofmThe reflection coefficient is the order m.
Initial value: prediction error
Figure BDA0003318399390000072
Initial value e of front and back prediction error0(n)＝b0(n) ═ x (n). Calculating filter coefficients according to the Burg algorithm
Figure BDA0003318399390000073
Calculating prediction error power
ρm＝(1-|km|2)ρm-1 (8)
Computing output
Figure BDA0003318399390000074
Finally obtaining the minimum prediction error power rhopAnd model parameters ak。
The power spectral density is obtained by substituting formula (3), and the frequency f of each inter-harmonic component contained in the original data can be accurately obtained1,f2,f3,...,fn(but not the precise amplitude and phase of each frequency component).
Step 2, carrying out FFT analysis on the voltage sampling signal sequence according to the frequency of each inter-harmonic component obtained in the step 1 to obtain the amplitude and the phase of the real inter-harmonic;
the FFT analysis of inter-harmonic containing signals typically results in spectral leakage, which includes both long range leakage and short range leakage. The long-range leakage is mutual interference between signal spectrum side lobes caused by signal truncation and a small truncation window, the method is non-real-time analysis, and the window length can be large enough, so the long-range leakage can be ignored; short-range leakage refers to that due to unreasonable length of a truncation window, a barrier effect of a discrete spectrum is caused to generate a false inter-harmonic signal, and real inter-harmonics are hidden. According to the accurate inter-harmonic frequency obtained in the step 1, the method selects the appropriate length of the truncation window, so that the amplitude and the phase of the real inter-harmonic are obtained.
The specific steps of the step 2 comprise:
(1) let fa，fbIs f1、f2、f3、...、fnOf the 2 frequency components having the smallest difference.
Determining the two closest frequency components in the signal, denoted faAnd fbThe difference f between the two frequenciesab＝|fa-fbThe frequency resolution Δ f of the spectral lines should at least satisfy (take m spectral lines):
Figure BDA0003318399390000081
minimum number of data points N for Fourier analysis of a signalminComprises the following steps:
Figure BDA0003318399390000082
intercepting N data of an original signal, and meeting the following requirements: n is an integer multiple of 1024 and N>Nmin。
(2) Performing FFT analysis on the intercepted data x (n) to obtain the amplitude and the phase of each inter-harmonic;
Figure BDA0003318399390000083
because the maximum frequency range of human perception to flicker does not exceed 0.05-35 Hz, the frequency band of inter-harmonic waves concerned by the invention is 15-85 Hz; after filtering, only f with the inter-harmonic frequency band within the range of 15-85 Hz is reserved1、f2、f3、...、fnThe amplitude and phase of each inter-harmonic, i.e.:
ui(t)＝Ui sin(ωit+θi)
(3) the original signal can be expressed as:
Figure BDA0003318399390000084
in the formula, the first part is fundamental wave, and the second part is inter-harmonic wave.
And 3, obtaining the measurement result of voltage fluctuation and flicker caused by wind power after the original signal passes through a vision sensitivity weighting filter to simulate lamp-eye-brain frequency response characteristic according to the amplitude and the phase of the obtained real inter-harmonic wave obtained by calculation in the step 2.
The specific steps of the step 3 comprise:
(1) the original signal is passed through a perceptibility weighting filter to simulate a lamp-eye-brain frequency response characteristic as follows:
Figure BDA0003318399390000091
and obtaining the final measurement result values of voltage fluctuation and flicker caused by wind power:
Figure BDA0003318399390000092
in the formula: g is a constant of the gain, and G is a constant of the gain,
ω0i＝ω0-ωi，θ0i＝θ0-θi。
in the present embodiment, the present invention is explained in terms of a hardware configuration with reference to fig. 1. The voltage signal (1.1) and the current signal (1.2) firstly pass through a cabinet wiring terminal (1.3) and then are converted into analog signals (1.4), the analog signals pass through a sensor (1.5) and a signal conditioning circuit (1.6) and then are subjected to data acquisition by a data acquisition card (1.7), the acquired data are sent to an industrial personal computer (1.8), and data analysis, calculation and storage are carried out by a LabVIEW software program.
The inter-harmonic detection device adopts a concept design software system of hierarchical structure design, and the structure is shown in fig. 2. The software device is established on a hardware platform (2.2) of the virtual instrument, and the equipment management of the hardware platform is realized through a Windows NT operating system (2.3). Data (2.1) acquired by a data acquisition card is input into device software, and the device software consists of a PCI equipment driver (2.4), an NI-DAQ data acquisition operation support library (2.5), a data analysis subsystem (2.6), a data storage subsystem (2.7) and a user interface (2.8). The data analysis subsystem completes complex operation on the acquired data, the data storage subsystem is used for storing the acquired field data and analysis results, and the user interface provides interface elements such as curves, charts, reports, buttons, menus, shortcut keys and the like on the front panel for a user to realize humanized operation.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.

Claims (4)

1.一种风电引起电压波动与闪变的测量方法，其特征在于：包括以下步骤：1. a method for measuring voltage fluctuation and flicker caused by wind power, is characterized in that: comprise the following steps: 步骤1、利用基于最优窗加权修正的Burg算法对，对同步采样后的风电输出电压信号序列进行功率谱估计，并通过功率谱的分析可精确得到各间谐波分量的频率；Step 1. Use the Burg algorithm pair based on the optimal window weighting correction to estimate the power spectrum of the synchronously sampled wind power output voltage signal sequence, and accurately obtain the frequency of each interharmonic component through the analysis of the power spectrum; 步骤2、根据步骤1所得出的各间谐波分量的频率，对电压采样信号序列进行FFT分析，获得真实间谐波的幅值与相位；Step 2, according to the frequency of each interharmonic component obtained in step 1, perform FFT analysis on the voltage sampling signal sequence to obtain the amplitude and phase of the real interharmonic; 步骤3、根据步骤2计算获得的获得真实间谐波的幅值与相位，将原始信号经过视感度加权滤波器模拟灯-眼-脑频率响应特性后，获得从而得到由风电引起电压波动与闪变的测量结果。Step 3. According to the amplitude and phase of the real interharmonics obtained by calculation in step 2, the original signal is passed through the visual sensitivity weighting filter to simulate the lamp-eye-brain frequency response characteristic to obtain the voltage fluctuation and flicker caused by wind power. variable measurement results. 2.根据权利要求1所述的一种风电引起电压波动与闪变的测量方法，其特征在于：所述步骤1的具体步骤包括：2. The method for measuring voltage fluctuation and flicker caused by wind power according to claim 1, wherein the specific steps of the step 1 include: (1)对于一个离散信号x(n)，表示成p阶AR模型为：(1) For a discrete signal x(n), the p-order AR model is expressed as:
Figure FDA0003318399380000011
Figure FDA0003318399380000011
 式(1)中：η(n)为零均值方差为σ2的白噪声。ak(k＝1，...，p)为p阶AR模型的系数。In formula (1): η(n) is white noise with zero mean variance σ 2 . a k (k=1, . . . , p) are the coefficients of the p-order AR model. (2)信号x(n)的AR功率谱密度为：(2) The AR power spectral density of the signal x(n) is:
Figure FDA0003318399380000012
Figure FDA0003318399380000012
 一个p阶的AR模型等效于一个p阶的线性预测器，AR模型的参数是线性预测器的系数，方差σ2等于阶次为p时最小预测误差功率ρp，故功率谱公式等价为：A p-order AR model is equivalent to a p-order linear predictor. The parameters of the AR model are the coefficients of the linear predictor, and the variance σ 2 is equal to the minimum prediction error power ρ p when the order is p , so the power spectrum formula is equivalent. for:
Figure FDA0003318399380000021
Figure FDA0003318399380000021
 所以只要求得最小预测误差功率ρp和模型参数ak，就能得到信号的功率谱；Therefore, only the minimum prediction error power ρ p and the model parameters ak are required to obtain the power spectrum of the signal; (3)AR模型参数求解(3) AR model parameter solution
Figure FDA0003318399380000022
Figure FDA0003318399380000022
Figure FDA0003318399380000023
Figure FDA0003318399380000023
Figure FDA0003318399380000024
Figure FDA0003318399380000024
 式(4)(5)(6)中em(n)、bm(n)分别是阶次为m时的前、后向预测误差；ρm为阶次为m时的预测误差功率；km为阶次为m时的反射系数。In formula (4) (5) (6), em (n) and b m (n) are the forward and backward prediction errors when the order is m , respectively; ρ m is the prediction error power when the order is m; k m is the reflection coefficient when the order is m. 初始值：预测误差
Figure FDA0003318399380000025
前后预测误差初始值e0(n)＝b0(n)＝x(n)。根据Burg算法计算滤波器系数：Initial value: prediction error
Figure FDA0003318399380000025
The initial value of the forward and backward prediction errors e 0 (n)=b 0 (n)=x(n). Calculate the filter coefficients according to Burg's algorithm:
Figure FDA0003318399380000026
Figure FDA0003318399380000026
 计算预测误差功率：Calculate prediction error power: ρm＝(1-|km|2)ρm-1 (8)ρ m =(1-|k m | 2 )ρ m-1 (8) 计算输出Calculate output
Figure FDA0003318399380000027
Figure FDA0003318399380000027
 最后得到最小预测误差功率ρp和模型参数ak。Finally, the minimum prediction error power ρ p and the model parameters ak are obtained. 代入式(3)即得功率谱密度，可精确得出原始数据中含有的各间谐波分量的频率f1,f2,f3,...,fn。Substitute into formula (3) to obtain the power spectral density, and the frequencies f 1 , f 2 , f 3 ,...,f n of each interharmonic component contained in the original data can be accurately obtained. 3.根据权利要求1所述的一种风电引起电压波动与闪变的测量方法，其特征在于：所述步骤2的具体步骤包括：3. The method for measuring voltage fluctuation and flicker caused by wind power according to claim 1, wherein the specific steps of the step 2 include: (1)令fa，fb为f1、f2、f3、...、fn中，差值最小的2个频率分量；(1) Let f a , f b be the two frequency components with the smallest difference among f 1 , f 2 , f 3 , ..., f n ; 确定信号中最接近的两个频率成分，记为fa和fb，则两频率的差值fab＝|fa-fb|，谱线的频率分辨率Δf至少应该满足：Determine the two closest frequency components in the signal, denoted as f a and f b , then the difference between the two frequencies f ab = |f a -f b |, the frequency resolution Δf of the spectral line should at least satisfy:
Figure FDA0003318399380000031
Figure FDA0003318399380000031
 对信号进行傅里叶分析的最小数据点数Nmin为：
Figure FDA0003318399380000032
The minimum number of data points N min for Fourier analysis of the signal is:
Figure FDA0003318399380000032
 对原始信号截取N个数据，满足：N为1024整数倍且N>Nmin；Intercept N pieces of data from the original signal, satisfying: N is an integer multiple of 1024 and N>N min ; (2)对截取后数据x(n)进行FFT分析，得到各次间谐波的幅值与相位；(2) Perform FFT analysis on the intercepted data x(n) to obtain the amplitude and phase of each interharmonic;
Figure FDA0003318399380000033
Figure FDA0003318399380000033
 由于人对闪变的最大觉察频率范围不会超过0.05～35Hz，因此，本发明关注的间谐波频段为15～85Hz；经过滤波后，仅保留间谐波频段为15～85Hz范围内的f1、f2、f3、...、fn各次间谐波的幅值与相位，即：Since the maximum frequency range of human perception of flicker will not exceed 0.05-35 Hz, the inter-harmonic frequency band concerned by the present invention is 15-85 Hz; after filtering, only the inter-harmonic frequency band within the range of 15-85 Hz is retained The amplitude and phase of each interharmonic of 1 , f 2 , f 3 ,..., f n , namely: ui(t)＝Ui sin(ωit+θi)u i (t)=U i sin(ω i t+θ i ) (3)则原始信号可表示为：(3) Then the original signal can be expressed as:
Figure FDA0003318399380000034
Figure FDA0003318399380000034
 式中，第一部分为基波，第二部分为间谐波。In the formula, the first part is the fundamental wave, and the second part is the interharmonic. 4.根据权利要求1所述的一种风电引起电压波动与闪变的测量方法，其特征在于：所述步骤3的具体步骤包括：4. The method for measuring voltage fluctuation and flicker caused by wind power according to claim 1, wherein the specific steps of step 3 include: (1)将原始信号经过下式的视感度加权滤波器模拟灯-眼-脑频率响应特性：(1) Simulate the lamp-eye-brain frequency response characteristics by passing the original signal through the visual sensitivity weighting filter of the following formula:
Figure FDA0003318399380000041
Figure FDA0003318399380000041
 并得到最终由风电引起电压波动与闪变的测量结果值：And get the final measurement result value of voltage fluctuation and flicker caused by wind power:
Figure FDA0003318399380000042
Figure FDA0003318399380000042
 式中：G为增益常数，Where: G is the gain constant, ω0i＝ω0-ωi，θ0i＝θ0-θi。ω 0i =ω 0 -ω i , θ 0i =θ 0 -θ i .
CN202111238621.XA 2021-10-25 2021-10-25 Method for measuring voltage fluctuation and flicker caused by wind power Pending CN113866493A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111238621.XA CN113866493A (en) 2021-10-25 2021-10-25 Method for measuring voltage fluctuation and flicker caused by wind power

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111238621.XA CN113866493A (en) 2021-10-25 2021-10-25 Method for measuring voltage fluctuation and flicker caused by wind power

Publications (1)

Publication Number Publication Date
CN113866493A true CN113866493A (en) 2021-12-31

Family

ID=78997717

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111238621.XA Pending CN113866493A (en) 2021-10-25 2021-10-25 Method for measuring voltage fluctuation and flicker caused by wind power

Country Status (1)

Country Link
CN (1) CN113866493A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116865269A (en) * 2023-09-01 2023-10-10 山东泰开电力电子有限公司 Wind turbine generator system high harmonic compensation method and system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102288807A (en) * 2011-05-14 2011-12-21 苏州大学 Method for measuring electric network voltage flicker
CN105223434A (en) * 2015-08-27 2016-01-06 国网青海省电力公司电力科学研究院 Quality of power supply mixing detection method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102288807A (en) * 2011-05-14 2011-12-21 苏州大学 Method for measuring electric network voltage flicker
CN105223434A (en) * 2015-08-27 2016-01-06 国网青海省电力公司电力科学研究院 Quality of power supply mixing detection method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
张全明 等: "基于频谱分析的间谐波闪变效应计算", 电力系统自动化, 10 May 2009 (2009-05-10), pages 67 - 70 *
张惠娟 等: "基于AR模型的电力系统间谐波分析", 电工技术学报, 31 July 2010 (2010-07-31), pages 144 - 145 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116865269A (en) * 2023-09-01 2023-10-10 山东泰开电力电子有限公司 Wind turbine generator system high harmonic compensation method and system
CN116865269B (en) * 2023-09-01 2023-11-21 山东泰开电力电子有限公司 Wind turbine generator system high harmonic compensation method and system

Similar Documents

Publication Publication Date Title
Zygarlicki et al. A reduced Prony's method in power-quality analysis—parameters selection
Zhang et al. The algorithm of interpolating windowed FFT for harmonic analysis of electric power system
Chen et al. Extended real model of Kalman filter for time-varying harmonics estimation
Tomic et al. A new power system digital harmonic analyzer
CN106018956B (en) A kind of power system frequency computational methods of adding window spectral line interpolation
Wen et al. Novel three-point interpolation DFT method for frequency measurement of sine-wave
CN101113995A (en) Fundamental wave and harmonic detection method based on Nuttall window bimodal interpolation FFT
CN108535613B (en) A voltage flicker parameter detection method based on combined window function
CN109061302A (en) A kind of wind power generator incorporated in power network group harmonic measure system converted based on EEMD and Hilbert
CN112213560A (en) A high-precision power grid broadband signal measurement method based on Z-ADALINE
CN108490257B (en) A Short-time Window Interharmonic Measurement Method Based on Spectral Curve Fitting
CN101216512A (en) A real-time high-precision detection method for non-sinusoidal periodic signals
CN114487589B (en) Power grid broadband signal adaptive measurement method, device and system
CN110320400B (en) Voltage flicker envelope parameter extraction method for quasi-synchronous sampling and improved energy operator
CN109541312A (en) A kind of new energy collects the subsynchronous harmonic detecting method in area
CN117872192B (en) A fast measurement method for impedance spectrum of energy storage battery
CN111984920A (en) Subsynchronous/supersynchronous harmonic parameter identification method, subsynchronous/supersynchronous harmonic parameter identification device, subsynchronous/supersynchronous harmonic parameter identification equipment and medium
Jin et al. A novel power harmonic analysis method based on Nuttall-Kaiser combination window double spectrum interpolated FFT algorithm
Mei et al. Wavelet packet transform and improved complete ensemble empirical mode decomposition with adaptive noise based power quality disturbance detection
CN113866493A (en) Method for measuring voltage fluctuation and flicker caused by wind power
CN109581045B (en) Inter-harmonic power metering method meeting IEC standard framework
Rodrigues et al. Low-cost embedded measurement system for power quality frequency monitoring
CN114184838B (en) Power system harmonic detection method, system and medium based on SN interconvolution window
CN112946374B (en) Three-phase unbalance detection method and device based on convolution window function
CN114689040A (en) Estimation method of pulsar rotation frequency based on prior information of pulsar radiation signal

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20211231